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Report that provides practical advice for bridging CS and Law in academia 

This work examines privacy laws and regulations that limit disclosure of personal data, and explores whether and how these restrictions apply when participants use cryptographically secure multi-party computation (MPC). By protecting data during use, MPC offers the promise of conducting data science in a way that (in some use cases) meets or even exceeds most people’s conceptions of data privacy. With MPC, it is possible to correlate individual records across multiple datasets without revealing the underlying records, to conduct aggregate analysis across datasets which parties are otherwise unwilling to share for competitive reasons, and to analyze aggregate statistics across datasets which no individual party may lawfully hold. However, most adoptions of MPC to date involve data that is not subject to privacy protection under the law. We posit that a major impediment to the adoption of MPC—on the data that society has deemed most worthy of protection—is the difficulty of mapping this new technology onto the design principles of data privacy laws. While a computer scientist might reasonably believe that transforming any data analysis into its privacy-protective variant using MPC is a clear win, we show in this work that the technological guarantees of MPC do not directly imply compliance with privacy laws. Specifically, a lawyer will likely want to ask several important questions about the pre-conditions that are necessary for MPC to succeed, the risk that data might inadvertently or maliciously be disclosed to someone other than the output party, and what recourse to take if this bad event occurs. We have two goals for this work: explaining why the privacy law questions are nuanced and that the lawyer is correct to proceed cautiously, and providing a framework that lawyers can use to reason systematically about whether and how MPC implicates data privacy laws in the context of a specific use case. Our framework revolves around three questions: a definitional question on whether the encodings still constitute ‘personal data,’ a process question about whether the act of executing MPC constitutes a data disclosure event, and a liability question about what happens if something goes wrong. We conclude by providing advice to regulators and suggestions to early adopters to spur uptake of MPC. It is our hope that this work provides the first step toward a methodology that organizations can use when contemplating the use of MPC. 

Software applications that employ secure multi-party computation (MPC) can empower individuals and organizations to benefit from privacy-preserving data analyses when data sharing is encumbered by confidentiality concerns, legal constraints, or corporate policies. MPC is already being incorporated into software solutions in some domains; however, individual use cases do not fully convey the variety, extent, and complexity of the opportunities of MPC. This position paper articulates a role-based perspective that can provide some insight into how future research directions, infrastructure development and evaluation approaches, and deployment practices for MPC may evolve. Drawing on our own lessons from existing real-world deployments and the fundamental characteristics of MPC that make it a compelling technology, we propose a role-based conceptual framework for describing MPC deployment scenarios. Our framework acknowledges and leverages a novel assortment of roles that emerge from the fundamental ways in which MPC protocols support federation of functionalities and responsibilities. Defining these roles using the new opportunities for federation that MPC enables in turn can help identify and organize the capabilities, concerns, incentives, and trade-offs that affect the entities (software engineers, government regulators, corporate executives, end-users, and others) that participate in an MPC deployment scenario. This framework can not only guide the development of an ecosystem of modular and composable MPC tools, but can make explicit some of the opportunities that researchers and software engineers (and any organizations they form) have to differentiate and specialize the artifacts and services they choose to design, develop, and deploy. We demonstrate how this framework can be used to describe existing MPC deployment scenarios, how new opportunities in a scenario can be observed by disentangling roles inhabited by the involved parties, and how this can motivate the development of MPC libraries and software tools that specialize not by application domain but by role. 

Secure Multi-Party Computation (MPC) allows mutually distrusting parties to run joint computations without revealing private data. Current MPC algorithms scale poorly with data size, which makes MPC on "big data" prohibitively slow and inhibits its practical use. Many relational analytics queries can maintain MPC's end-to-end security guarantee without using cryptographic MPC techniques for all operations. Conclave is a query compiler that accelerates such queries by transforming them into a combination of data-parallel, local cleartext processing and small MPC steps. When parties trust others with specific subsets of the data, Conclave applies new hybrid MPC-cleartext protocols to run additional steps outside of MPC and improve scalability further. Our Conclave prototype generates code for cleartext processing in Python and Spark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclave scales to data sets between three and six orders of magnitude larger than state-of-the-art MPC frameworks support on their own. Thanks to its hybrid protocols, Conclave also substantially outperforms SMCQL, the most similar existing system. 

An essential component of initiatives that aim to address pervasive inequalities of any kind is the ability to collect empirical evidence of both the status quo baseline and of any improvement that can be attributed to prescribed and deployed interventions. Unfortunately, two substantial barriers can arise preventing the collection and analysis of such empirical evidence: (1) the sensitive nature of the data itself and (2) a lack of technical sophistication and infrastructure available to both an initiative’s beneficiaries and to those spearheading it. In the last few years, it has been shown that a cryptographic primitive called secure multi-party computation (MPC) can provide a natural technological resolution to this conundrum. MPC allows an otherwise disinterested third party to contribute its technical expertise and resources, to avoid incurring any additional liabilities itself, and (counterintuitively) to reduce the level of data exposure that existing parties must accept to achieve their data analysis goals. However, achieving these benefits requires the deliberate design of MPC tools and frameworks whose level of accessibility to non-technical users with limited infrastructure and expertise is state-of-the-art. We describe our own experiences designing, implementing, and deploying such usable web applications for secure data analysis within the context of two real-world initiatives that focus on promoting economic equality.